Materials informatics (MI), which emerges from the integration of materials science and data science, is expected to greatly streamline material discovery and development. The data used for MI are obtained from both computational and experimental studies, while their integration remains challenging. In our previous study, we reported the integration of these datasets by applying a machine learning model that captures trends hidden in the experimental datasets to compositional data stored in the computational database. In this study, we use the obtained data to construct materials maps, which visualize the relation in the structural features of materials, aiming to support study by the experimental researchers. The map is constructed using a MatDeepLearn (MDL) framework, which implements the graph-based representation of material structures, deep learning, and dimensional reduction for map construction. We evaluate the obtained materials maps through statistical analysis and found that MDL using message passing neural network (MPNN) architecture enables efficient extraction of features that reflect the structural complexity of materials. Moreover, we found that this advantage does not necessarily translate into improved accuracy in the prediction of material properties. We assume this unexpected outcome to the high learning performance inherent in MPNN, which can contribute to the structuring of data points within the materials map.

The scarcity of property labels remains a key challenge in materials informatics, whereas materials data without property labels are abundant in comparison. By pretraining supervised property prediction models on self-supervised tasks that depend only on the "intrinsic information" available in any Crystallographic Information File (CIF), there is potential to leverage the large amount of crystal data without property labels to improve property prediction results on small datasets. We apply Deep InfoMax as a self-supervised machine learning framework for materials informatics that explicitly maximises the mutual information between a point set (or graph) representation of a crystal and a vector representation suitable for downstream learning. This allows the pretraining of supervised models on large materials datasets without the need for property labels and without requiring the model to reconstruct the crystal from a representation vector. We investigate the benefits of Deep InfoMax pretraining implemented on the Site-Net architecture to improve the performance of downstream property prediction models with small amounts (<10^3) of data, a situation relevant to experimentally measured materials property databases. Using a property label masking methodology, where we perform self-supervised learning on larger supervised datasets and then train supervised models on a small subset of the labels, we isolate Deep InfoMax pretraining from the effects of distributional shift. We demonstrate performance improvements in the contexts of representation learning and transfer learning on the tasks of band gap and formation energy prediction. Having established the effectiveness of Deep InfoMax pretraining in a controlled environment, our findings provide a foundation for extending the approach to address practical challenges in materials informatics.

This paper elucidates the challenges and opportunities inherent in integrating data-driven methodologies into geotechnics, drawing inspiration from the success of materials informatics. Highlighting the intricacies of soil complexity, heterogeneity, and the lack of comprehensive data, the discussion underscores the pressing need for community-driven database initiatives and open science movements. By leveraging the transformative power of deep learning, particularly in feature extraction from high-dimensional data and the potential of transfer learning, we envision a paradigm shift towards a more collaborative and innovative geotechnics field. The paper concludes with a forward-looking stance, emphasizing the revolutionary potential brought about by advanced computational tools like large language models in reshaping geotechnics informatics.

Artificial intelligence is changing the paradigm for many industries, and materials-focused commerce is no exception, where tremendous opportunities lie ahead. With the success of effective and generalizable deep learning tools, the materials industry is primed to take advantage of unprecedented breakthroughs, leveraging materials modeling, analysis, and design toward a more efficient, less costly, and more versatile response to market demands and opportunities, through materiomics. With data available from autonomous experimentation, large databases like the Materials Project within the Materials Genome initiative, or synthetic data, there exist many opportunities to accelerate and expand your materials design platform. Today, practicing engineers are expected to have both domain knowledge and a solid understanding of modern machine learning tools. This course will teach all the fundamentals necessary for you to reach the next milestone in practicing materiomics, by navigating the complex world of AI.

"Materials Informatics is an R&D paradigm shift; enabling discoveries and cutting the time to market" Nearly every sector is proposing the use of artificial intelligence. Materials science R&D is relatively late to this trend, and there are many industry-specific hurdles, but the opportunities are beginning to be realised and the potential impact is significant. Materials informatics is the use of data-centric approaches for materials science discovery and development. This is principally enabled by improved data infrastructures and machine learning solutions; this is set to be a paradigm shift in the way researchers conduct R&D projects and a discussion on why the adoption is now can be seen in a previous article. At this key moment of initial commercial adoption, ID Tech Ex has released the most comprehensive technical market report on the topic, 'Materials Informatics 2020-2030'.